Hung Y.-CTsai W.-CYang S.-FChuang S.-CTseng Y.-K.YING-CHAO HUNG2022-11-112022-11-11201209591524https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857189996&doi=10.1016%2fj.jprocont.2011.12.009&partnerID=40&md5=05dcdb2999c2e7432912afb89d486e3fhttps://scholars.lib.ntu.edu.tw/handle/123456789/625030Profile monitoring has received increasingly attention in a wide range of applications in statistical process control (SPC). In this work, we propose a framework for monitoring nonparametric profiles in multi-dimensional data spaces. The framework has the following important features: (i) a flexible and computationally efficient smoothing technique, called Support Vector Regression, is employed to describe the relationship between the response variable and the explanatory variables; (ii) the usual structural assumptions on the residuals are not required; and (iii) the dependence structure for the within-profile observations is appropriately accommodated. Finally, real AIDS data collected from hospitals in Taiwan are used to illustrate and evaluate our proposed framework. © 2011 Elsevier Ltd. All rights reserved.Block bootstrap; Confidence region; Nonparametric profile monitoring; Support Vector Regression[SDGs]SDG3Block bootstrap; Computationally efficient; Confidence region; Explanatory variables; Multidimensional data; Non-parametric; Profile monitoring; Smoothing techniques; Structural assumption; Support Vector Regression; Support vector regressions; Statistical process control; Statistical methodsjournal article10.1016/j.jprocont.2011.12.0092-s2.0-84857189996